Deep neural networks work remarkably well on a variety of applications, specifically when the test samples are drawn from the same distribution as the training data.
The performance falls drastically when there is a non-trivial shift between the train and test distributions [1]}, [2]}.
In several real-world settings, such a performance drop may make the model unusable.
There have been recent works in the literature to train robust models [3]}, [4]}, [5]}. While this is a viable research direction for the problem at hand, it entails modifying the training process.
This may not always be practical as the training data may no longer be available due to privacy/storage concerns.
All that is available is a previously trained model.
Therefore, there is a growing interest in Test Time Adaptation (TTA), where the models can be adapted at test time, without changing the training process or requiring access to the original training data.
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